GroupSegment-SHAP (GS‑SHAP) introduces explanatory units called group‑segment players that capture cross‑variable dependence and distribution shifts over time in multivariate time‑series models. By attributing Shapley values to these units, GS‑SHAP preserves joint structural signals that traditional time‑series SHAP variants fragment. Experiments on human activity recognition, power‑system forecasting, medical signal analysis, and financial time series show that GS‑SHAP improves deletion‑based faithfulness by about 1.7× and reduces runtime by roughly 40% compared to existing baselines, while a financial case study demonstrates its ability to reveal interpretable multivariate‑temporal interactions during high‑volatility periods.
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